User-Friendly Differential Voltage Analysis Freeware for the Analysis of Degradation Mechanisms in Li-Ion Batteries
Bibliographic record
Abstract
A user-friendly differential voltage analysis software has been developed and is described here. High-precision reference potential-specific capacity data for Li/negative electrode and Li/positive electrodes, as well as the cycled full cell potential-specific capacity, must be supplied by the user. From these, the differential voltage versus capacity, dV/dQ vs. Q, of a full Li-ion cell is calculated and compared to experiment. The calculated dV/dQ vs. Q curve has four adjustable parameters which are optimized manually with slider bars or automatically by least squares fitting of the calculation to experiment. The parameters are the positive electrode mass, the negative electrode mass, the positive electrode slippage and the negative electrode slippage. Examples of the use of the program are given for graphite/LiCoO 2 wound cells cycled for hundreds of cycles. The variation of the four parameters with cycle number give insights into the mechanisms of cell failure equivalent to that which could be obtained with a Li reference electrode inserted within the cell. The software is available free of charge by contacting the authors.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.107 | 0.027 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".